What does RAG stand for?

RAG stands for retrieval-augmented generation. It is a pattern in which a system first retrieves relevant source material, then passes that material to a large language model as context so the generated answer is grounded in specific documents rather than only in what the model learned during training.

How does RAG work step by step?

A question is converted into a vector embedding. That vector is matched against a store of embedded documents to find the closest passages. The retrieved passages are inserted into the prompt alongside the original question. The model then generates an answer from that supplied context, and the system can cite which passages it drew on.

Why use RAG instead of fine-tuning?

Fine-tuning changes the model's weights and suits teaching a consistent style or format. RAG leaves the model alone and changes what it is shown at question time, which suits facts that change often. For CRM data that updates hourly, RAG is the practical choice: a new Case is retrievable immediately, with no retraining cycle.

How does RAG work with Salesforce data?

GPTfy retrieves from the records a user already has permission to see, such as Cases, Knowledge Articles, Opportunities and related files, so retrieval respects Salesforce sharing rules. PII masking is applied to the retrieved text before the callout, and the prompt and response are recorded as a Security Audit Record for review.

Does RAG stop an AI from hallucinating?

It reduces hallucination but does not eliminate it. Supplying real source passages gives the model correct material to work from and makes answers checkable against a citation. The model can still misread a passage or fill a gap when retrieval returns nothing useful, so grounded answers still need review in high-stakes workflows.

RAG (Retrieval-Augmented Generation)

An LLM is given relevant retrieved documents as context before generating a response, grounding outputs in your specific data, not just the model's training.

Quick answer

What is RAG (Retrieval-Augmented Generation)?

RAG, or retrieval-augmented generation, is a pattern that fetches relevant documents and places them in the prompt before the model answers, so the response is grounded in your data rather than the model's training. In Salesforce, GPTfy retrieves Cases, Knowledge Articles and related records, then masks PII before the callout.

Last updated: August 2026

RAG is the standard pattern for making LLMs useful with company-specific data. The flow: (1) user asks a question; (2) the question is embedded as a vector; (3) the system retrieves the K most similar documents from a vector database; (4) retrieved documents are stuffed into the prompt as context; (5) the LLM generates a grounded response.

For Salesforce, RAG enables answering questions like "What's the history of issues with Acme Corp?" The system retrieves the relevant Cases, Knowledge Articles, and email threads, then the LLM synthesizes a response. Without RAG, the LLM would either fabricate or refuse to answer.

Modern RAG variations include: hybrid retrieval (combining vector and keyword search), reranking (using a second model to refine retrieval), and GraphRAG (using knowledge graphs alongside vectors). gptfy's RAG-in-Salesforce feature implements production-grade RAG with PII masking and audit trails over Salesforce data.

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